Current alignment techniques primarily focus on producing aligned behavior during training or post-training. However, frontier models increasingly undergo continual optimization through fine-tuning, reinforcement learning, preference optimization, and domain adaptation. While these methods improve capabilities and specialization, relatively little work has investigated whether alignment itself remains stable throughout future optimization or gradually degrades as models continue to learn.

This project investigates whether alignment should be treated as a simple emergent property stored in distributed parameters, or should it also be treated as a property which is actively preserved throughout future optimisation of the model.

The central hypothesis is that current neural networks rely almost entirely on their parameters to encode their previously learned behavior. While this has proven highly effective for learning capabilities restricted to vision, autoregressive text and audio models, it may make desirable safety behavior inherently fragile under continued optimization. Subsequent optimization may unintentionally or intentionally alter previously learned alignment-relevant behaviors for the worse. Existing continual learning methods primarily aim to preserve task performance across multiple tasks but they are not explicitly designed to preserve alignment-relevant behaviors or safety properties.

The Proposal:

We investigate whether explicit retrieval of semantically related alignment-relevant behaviors before optimization can reduce alignment drift during continual learning. Rather than constraining individual parameters, which ignores the highly coupled nature of modern neural networks, we investigate mechanisms that constrain the behavioral consequences of updates. The objective is not to freeze model behavior, but to permit adaptation while preserving previously established alignment properties.

Importantly this project does not begin by proposing a new production architecture or claiming a complete solution to malicious fine-tuning. Instead it asks a more fundamental research question:

Can explicit behavioral memory measurably improve the persistence of alignment-relevant behavior during future optimization and can it prove to be a significant boost in preventing models from learning misaligned behavior from bad-quality data?

The project will develop a prototype framework based on behavioral memory and evaluate it against existing approaches from continual learning and catastrophic forgetting for achieving alignment persistence during continual optimization.. The experiments will investigate whether explicit retrieval or semantically related previous behavior improves resistance to alignment drift under continual learning, fine-tuning while maintaining adaptation to new information.

The project will be a form of continuation of the work done by me during the non-trivial research foundation program and through the non-trivial fellowship selection process. It will build upon it with the help of mentors/researchers from Texas A&M as well as IIT Roorkee.

Who will be working on this project?

The research will be undertaken mostly by me but will be occasionally provided with assistance in terms of mentorship, guidance, and network connections by my mentors and professors I am interning under in Texas A&M and IIT Roorkee.

What concrete output does this research guarantee?

The output of this research is not based around a single methodology or ideal. The output will be two-fold:

1. The research aims to develop an evaluation framework for measuring alignment persistence under continual optimization in AI models and measuring the amount of effort required to surpass these guard rails and safety measures during the process.
2. The concrete output I wish to establish is a prototype behavioral-memory framework, which allows for better alignment of models and to separate out the difference between catastrophic forgetting of alignment related behaviours and continual learning of new knowledge.